Multiple Criteria Linear Programming

A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis

Expert Systems with Applications ◽

10.1016/j.eswa.2008.11.007 ◽

2009 ◽

Vol 36 (5) ◽

pp. 8932-8937 ◽

Cited By ~ 42

Author(s):

Zhiwang Zhang ◽

Yong Shi ◽

Guangxia Gao

Keyword(s):

Linear Programming ◽

Rough Set ◽

Medical Diagnosis ◽

Multiple Criteria ◽

Programming Approach ◽

Linear Programming Approach ◽

Diagnosis And Prognosis

A Classification Model for a Two-Class (New Product Purchase) Discrimination Process Using Multiple-Criteria Linear Programming

Data Mining Methods and Applications ◽

10.1201/b15783-19 ◽

2007 ◽

pp. 295-304

Author(s):

Dinesh Pai ◽

Sheila Lawrence

Keyword(s):

Linear Programming ◽

New Product ◽

Multiple Criteria ◽

Classification Model

Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v27i5.5597 ◽

2011 ◽

Vol 27 (5) ◽

pp. 73 ◽

Cited By ~ 2

Author(s):

Wikil Kwak ◽

Susan Eldridge ◽

Yong Shi ◽

Gang Kou

Keyword(s):

Data Mining ◽

Linear Programming ◽

Financial Distress ◽

Multiple Criteria ◽

Financial Statement ◽

Font Size ◽

Explanatory Variables ◽

Data Mining Approach ◽

Starting Point ◽

Accuracy Rates

<h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;">Our study evaluates a multiple criteria linear programming (MCLP) and other data mining approaches to predict auditor changes using a portfolio of financial statement measures to capture financial distress. The results of the MCLP approach and the other data mining approaches show that these methods perform reasonably well to predict auditor changes using financial distress variables. Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent. Our study is designed to establish a starting point for auditor-change prediction using financial distress variables. Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</h1>

MODEL ABC MULTI-CRITERIA INVENTORY CLASSIFICATION (MCIC) MENGGUNAKAN PEMROGRAMAN LINEAR PADA PRODUK KIMIA

JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK ◽

10.30988/jmil.v3i2.242 ◽

2019 ◽

Vol 3 (2) ◽

pp. 106-118

Author(s):

Eko Pratomo (Politeknik APP - Indonesia)

Keyword(s):

Linear Programming ◽

Lead Time ◽

Multiple Criteria ◽

Traditional Model ◽

Abc Model ◽

Linear Programming Method ◽

Inventory Policies ◽

Chemical Product ◽

Class A ◽

Class B

Abstract In order to maintain their inventory efficiently, enterprises need to prioritize inventory policies considering multiple criteria. A Multi Criteria Inventory Classification (MCIC) is one of the most effective techniques widely used to classify inventory. In this paper, multiple criteria (annual value, lead time, cost per unit) are considered on ABC inventory classification. The aim of this study is classify products considering those multiple criteria. Multiple criteria ABC Classifications methodology developed by Ramanathan-Model and Ng-Model are used and compared with traditional method. Data are collected from annual chemical product transaction on PT XYZ during 2018. In this paper, linear programming method is used to solve ABC MCIC Model. The result of this study show that 12 items (14%) are identified as Class A, 26 items (30%) as class B and the remaining 48 items (56%) as C Class. In our conclution, we propose inventory policies based on the result of the ABC Models. Keywords: ABC Model; MCIC; Traditional Model; Ramanathan-Model; Ng-Model; Linear Programming; Chemical Product.Abstrak Dalam mengelola persediaan secara efisien, perusahaan perlu menentukan prioritas pengelolaan persediaan dengan mempertimbangkan beberapa kriteria. Klasifikasi ABC Multi Kriteria (MCIC) merupakan model klasifikasi persediaan barang yang umum digunakan oleh perusahaan dalam mengelola persediaan dalam jumlah besar. Penelitian ini menggunakan multi kriteria berupa nilai total produk, lead time dan biaya per unit. Tujuan penelitian adalah mengelompokan jenis/kelas barang sesuai dengan tingkat kepentingan dengan mempertimbangkan multi kriteria. Metode Multi kriteria yang telah dikembangkan oleh Ramanathan-Model dan Ng-Model dibandingkan dengan hasil klasifikasi Single criteria ABC (Traditional model). Data yang digunakan adalah data tahunan transaksi produk kimia PT XYZ di tahun 2018. Penyelesaian model ABC multi kriteria (MCIC) dengan pemrograman linear. Terdapat 86 items produk kimia yang diklasifikasikan dengan hasil klasifikasi A sejumlah 12 item (14%), B sejumlah 26 item (30%) dan item C sejumlah 48 item (56%). Pada penelitan ini juga disampaikan kebijakan inventory masing-masing kelas berdasarkan hasil klasifikasi ABC model yang telah dilakukan.Kata Kunci: Model ABC; MCIC; Model tradisional; Model Ramanathan; Model Ng; Pemrograman Linear; Produk kimia.

Robust Efficient Basis of Interval Multiple Criteria and Multiple Constraint Level Linear Programming

Multi-Objective Programming and Goal Programming ◽

10.1007/978-3-540-36510-5_22 ◽

2003 ◽

pp. 165-170

Author(s):

Masaaki Ida

Keyword(s):

Linear Programming ◽

Multiple Criteria ◽

Multiple Constraint ◽

Constraint Level

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch004 ◽

2008 ◽

pp. 26-49 ◽

Cited By ~ 1

Author(s):

Yong Shi ◽

Yi Peng ◽

Gang Kou ◽

Zhengxin Chen

Keyword(s):

Data Mining ◽

Linear Programming ◽

Credit Card ◽

Programming Model ◽

Real Life ◽

Multiple Criteria ◽

Linear Programming Model ◽

Multiple Criteria Optimization ◽

Research Challenges ◽

Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2011070104 ◽

2011 ◽

Vol 7 (3) ◽

pp. 88-101 ◽

Cited By ~ 1

Author(s):

DongHong Sun ◽

Li Liu ◽

Peng Zhang ◽

Xingquan Zhu ◽

Yong Shi

Keyword(s):

Linear Programming ◽

Portfolio Management ◽

Real World ◽

Credit Card ◽

Extraction Method ◽

Large Scale ◽

Programming Model ◽

Rule Extraction ◽

Multiple Criteria ◽

Data Sets

Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated that RMCLP is effective and efficient in classifying large scale data sets. However, a possible limitation of RMCLP is poor interpretability and low comprehensibility for end users and experts. This deficiency has limited RMCLP’s use in many real-world applications where both accuracy and transparency of decision making are required, such as in Customer Relationship Management (CRM) and Credit Card Portfolio Management. In this paper, the authors present a clustering based rule extraction method to extract explainable and understandable rules from the RMCLP model. Experiments on both synthetic and real world data sets demonstrate that this rule extraction method can effectively extract explicit decision rules from RMCLP with only a small compromise in performance.

Multiple Criteria Group Decision Making Using a Parametric Linear Programming Technique for Multidimensional Analysis of Preference Under Uncertainty of Pythagorean Fuzziness

IEEE Access ◽

10.1109/access.2019.2957161 ◽

2019 ◽

Vol 7 ◽

pp. 174108-174128 ◽

Cited By ~ 2

Author(s):

Ting-Yu Chen

Keyword(s):

Decision Making ◽

Linear Programming ◽

Group Decision Making ◽

Group Decision ◽

Multiple Criteria ◽

Multidimensional Analysis ◽

Parametric Linear Programming ◽

Programming Technique ◽

Linear Programming Technique

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch001 ◽

2009 ◽

pp. 1-25

Author(s):

Yong Shi ◽

Yi Peng ◽

Gang Kou ◽

Zhengxin Chen

Keyword(s):

Data Mining ◽

Linear Programming ◽

Programming Model ◽

Neuronal Damage ◽

Real Life ◽

Multiple Criteria ◽

Linear Programming Model ◽

Multiple Criteria Optimization ◽

Research Challenges ◽

Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

Multiple criteria network models for project management

Agricultural Economics (Zemědělská ekonomika) ◽

10.17221/5169-agricecon ◽

2012 ◽

Vol 50 (No. 2) ◽

pp. 71-76 ◽

Cited By ~ 1

Author(s):

T. Šubrt

Keyword(s):

Linear Programming ◽

Project Management ◽

Programming Model ◽

Critical Path ◽

Network Models ◽

Multiple Criteria ◽

Linear Programming Model ◽

Software Support ◽

Task Duration ◽

Starting Point

The aim of the paper is to present one possibility of how to model and solve a resource oriented critical path problem. As a starting point, a single criteria model for critical path finding is shortly mentioned. Lately, more criteria functions for this model are defined. If any project task uses more resources for its completion, its duration usually depends on only one of them – other resources are not fully used. In here defined multiple criteria approach, these dependencies are not assumed. Each criteria function is derived from a theoretical task duration based on a number of units of only one resource and on its importance. Using either linear programming model with aggregated criteria function or simple Excel calculation with Microsoft Project software support, a so-called compromise critical path can be found. On this path, some resources are overallocated and some are underallocated but the total sum of all underallocations and all overallocations is minimized. All resources are used as effectively as possible and the project is as short as possible too.